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Proceedings Paper

HT-BONE: a graphical user interface for the identification of bone profiles in CT images via extended Hough transform
Author(s): Cristina Campi; Annalisa Perasso; Mauro C. Beltrametti; Michele Piana; Gianmario Sambuceti; Anna Maria Massone
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Paper Abstract

It has been recently proved that the computational analysis of X-ray Computed Tomography (CT) images allows clinicians to assess the alteration of compact bone asset due to hematological diseases. HT-BONE implements a new method, based on an extension of the Hough transform (HT) to a wide class of algebraic curves, for accurately measuring global and regional geometric properties of trabecular and compact bone districts. In the case of CT/PET analysis, the segmentation of the CT images provides masks for Positron Emission Tomography (PET) data, extracting the metabolic activity in the region surrounded by compact bone tissue. HT-BONE offers an intuitive, user-friendly, Matlab-based Graphical User Interface (GUI) for all input/output procedures and the automatic managing of the segmentation process also from non-expert users: the CT/PET data can be loaded and browsed easily and the only pre-preprocessing required from the user is the drawing of Regions Of Interest (ROIs) around the bone districts under consideration. For each bone district, specific families of curves, whose reliability has been already tested in previous works, is automatically selected for the recognition task via HT. As output, the software returns masks of the segmented compact bone regions, images of the Standard Uptake Values (SUV) in the masked regions of PET slices, and the values of the parameters in the curve equations utilized in the HT procedure. This information can be used for all pathologies and clinical conditions for which the alteration of the compact bone asset or bone marrow distribution plays a crucial role.

Paper Details

Date Published: 21 March 2016
PDF: 10 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978423 (21 March 2016); doi: 10.1117/12.2216375
Show Author Affiliations
Cristina Campi, Univ. di Roma la Sapienza (Italy)
Annalisa Perasso, SPIN, CNR (Italy)
Mauro C. Beltrametti, Univ. degli Studi di Genova (Italy)
Michele Piana, SPIN, CNR (Italy)
Univ. degli Studi di Genova (Italy)
Gianmario Sambuceti, IRCCS San Martino-IST (Italy)
Univ. degli Studi di Genova (Italy)
Anna Maria Massone, SPIN, CNR (Italy)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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